10 accepted papers
Though larger vessels may be well-equipped to deal with wavy conditions, smaller vessels are often more susceptible to disturbances. This paper explores the development of a nonlinear model predictive control (NMPC) system for Uncrewed Surface Vessels (USVs) in wavy conditions to minimize average ro…
Mobile robots are well suited for environmental surveys because they can travel to any area of interest and react to observations without the need for pre-existing infrastructure or significant setup time. However, vehicle motion constraints limit where and when measurements occur. This is challengi…
This paper proposes the Real-Time Fast Marching Tree (RT-FMT), a real-time planning algorithm that features local and global path generation, multiple-query planning, and dynamic obstacle avoidance. During the search, RT-FMT quickly looks for the global solution and, in the meantime, generates local…
Path planning and localization in low-light and inclement weather conditions are critical problems facing autonomous vehicle systems. Our proposed method applies a single modality, millimetre-wave radar perception system for the detection of roadside retro-reflectors. Radar-based perception tasks ca…
Spatiotemporal maps are data-driven estimates of time changing phenomena. For environmental science, rather than collect data from an array of static sensors, a mobile sensor platform could reduce setup time and cost, maintain flexibility to be deployed to any area of interest, and provide active fe…
We introduce a self-supervised method for systematically choosing traversable terrain while autonomously navigating a vehicle to a goal position in an unknown off-road environment. Leveraging the color discriminant bias of off-road terrain types, and using images from a vehicle-mounted camera, we em…
This paper presents a learning-based Model Predictive Control (MPC) methodology incorporating nonlinear predictions with robotics applications in mind. In particular, MPC is combined with feedback linearization for computational efficiency and Gaussian Process Regression (GPR) is used to model unkno…
This paper presents a path following method for autonomous wheeled vehicles that combines iterative learning control (ILC) with nonlinear feedback linearization (FBL) to provide anticipatory control action based on stored path following errors over repeated driving trials. By implementing ILC in a f…
An axis map (AM) represents the orientations of planar surfaces in an environment and is void of positional information. Three-dimensional axis mapping (3DAM) is a graph-based optimization algorithm that generates an AM, while carefully addressing the parameterization of axes (i.e., normal vectors).
Although passive sensors are widely used for many mobile robotics applications that perform mapping and localization functions, there are many environments (e.g., mining and planetary) where active sensors are more practical. However, at present, most 3D SLAM algorithms that do use LiDAR and/or time…